手臂脂肪、左腿脂肪和躯干脂肪块与多囊卵巢综合征风险的因果关系:一项孟德尔随机研究。

IF 1.6 4区 医学 Q4 BIOCHEMICAL RESEARCH METHODS
Yuhan Zhang, Wei Zhou, Qiong Su, Qi Chen
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引用次数: 0

摘要

背景:观察性研究发现,手臂脂肪、左腿脂肪和躯干脂肪块对多囊卵巢综合征(PCOS)有不同的影响。然而,它们之间的因果关系仍然未知:利用最大的全基因组关联研究(GWAS)的汇总数据,进行了双样本孟德尔随机化(MR)研究。随机效应反方差加权法(IVW)、加权中位数法(WM)和 MR-Egger 回归分析是采用的主要统计方法。最后,还进行了敏感性评估。Cochran's Q 检验用于分析异质性,MR-Egger 回归(截距项)用于分析水平多向性。为了评估 MR 估计值是否会受到表现出显著水平多义性的单核苷酸多态性(SNP)的影响,进行了撇除分析:结果:该研究发现,左腿脂肪量、手臂脂肪量和躯干脂肪量与多囊卵巢综合征的遗传因素之间存在明显的正相关性(几率比(OR):4.452,置信区间(OR):0.5):4.452,置信区间(CI):2.740-7.232,P < 0.001,OR:结论:本研究表明,手臂脂肪、左腿脂肪和躯干脂肪量可能与多囊卵巢综合征存在遗传相关性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Causal Association between Arm Fat, Left Leg Fat, and Trunk Fat Masses and Risk of Polycystic Ovarian Syndrome: A Mendelian Randomization Study.

Background: Observational studies have reported that arm fat, left leg fat, and trunk fat masses have different effects on polycystic ovarian syndrome (PCOS). However, the causal relationship between them remains unknown.

Materials and methods: A two-sample Mendelian randomization (MR) study was conducted by utilizing pooled data from the largest Genome-Wide Association Study (GWAS). Random effect inverse variance weighted (IVW) method, weighted median (WM), and MR-Egger regression analysis were the main statistical methods utilized. Finally, a sensitivity assessment was conducted. Cochran's Q test was used to analyze heterogeneity, whereas MR-Egger regression (intercept term) was used to analyze horizontal pleiotropy. The leave-one-out analysis was performed to assess if MR estimates were impacted by a single nucleotide polymorphism (SNP) exhibiting significant horizontal pleiotropy.

Results: This study discovered a significant positive correlation between left leg fat mass, arm fat mass, and trunk fat mass and genetic factors of PCOS (odds ratio (OR): 4.452, confidence interval (CI): 2.740-7.232, p < 0.001, OR: 3.321, CI: 2.248-4.907, p < 0.001, and OR: 2.518, CI: 1.722-3.682, p < 0.001, respectively).

Conclusion: This study indicates that arm fat, left leg fat, and trunk fat masses may be genetically correlated with PCOS.

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来源期刊
CiteScore
3.10
自引率
5.60%
发文量
327
审稿时长
7.5 months
期刊介绍: Combinatorial Chemistry & High Throughput Screening (CCHTS) publishes full length original research articles and reviews/mini-reviews dealing with various topics related to chemical biology (High Throughput Screening, Combinatorial Chemistry, Chemoinformatics, Laboratory Automation and Compound management) in advancing drug discovery research. Original research articles and reviews in the following areas are of special interest to the readers of this journal: Target identification and validation Assay design, development, miniaturization and comparison High throughput/high content/in silico screening and associated technologies Label-free detection technologies and applications Stem cell technologies Biomarkers ADMET/PK/PD methodologies and screening Probe discovery and development, hit to lead optimization Combinatorial chemistry (e.g. small molecules, peptide, nucleic acid or phage display libraries) Chemical library design and chemical diversity Chemo/bio-informatics, data mining Compound management Pharmacognosy Natural Products Research (Chemistry, Biology and Pharmacology of Natural Products) Natural Product Analytical Studies Bipharmaceutical studies of Natural products Drug repurposing Data management and statistical analysis Laboratory automation, robotics, microfluidics, signal detection technologies Current & Future Institutional Research Profile Technology transfer, legal and licensing issues Patents.
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